Rules and feature extraction for microcalcifications detection in digital mammograms using neuro-symbolic hybrid systems and undecimated filter banks

In this paper, we present a Neuro-Symbolic Hybrid System methodology to improve the recognition stage of benignant or malignant microcalcifications in mammography. At the first stage, we use five different undecimated filter banks in order to detect the microcalcifications. The microcalcifications appear as a small number of high intensity pixels compared with their neighbors. Once the microcalcifications were detected, we extract rules in order to obtain the image features. At the end, we can classify the microcalcification in one of three sets: benign, malign, and normal. The results obtained show that there is no a substantial difference in the number of detected microcalcification among the several filter banks used and the NSHS methodology proposed can improve, in the future, the results of microcalcification recognition.